Longitudinal systemic transcriptomic profiling and neuropathological deposition of neutrophil extracellular traps in Parkinson’s Disease

Published in Movement Disorders · Open research code · Parkinson’s disease and neuroinflammation

Authors

Huu Dat Nguyen, Engr., MMSc., Ph.D.^1,2,3*, Seungmin Lee, MD., Ph.D.⁴, Hyeo-Il Ma, MD., Ph.D.^1,2,3, Yun Joong Kim, MD., Ph.D.⁵, Han-Joon Kim, MD., Ph.D.⁴, Young Eun Kim, MD., Ph.D.^1,2,3,†

^†Corresponding author
^*First author, Lead contact

Affiliations

¹ Department of Neurology, Hallym University Sacred Heart Hospital, Hallym University, Anyang, Gyeonggi, Republic of Korea
² Laboratory of Parkinson’s Disease and Neurodegenerative disease, Hallym Institute for Translational Medicine, Anyang, Gyeonggi, Republic of Korea
³ Hallym Neurological Institute, Hallym University, Anyang, Gyeonggi, Republic of Korea
⁴ Department of Neurology, Seoul National University Hospital, Seoul, Republic of Korea
⁵ Department of Neurology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Gyeonggi, Republic of Korea

Contacts

Prof. Young Eun Kim, MD., Ph.D.

Huu Dat Nguyen, Engr., MMSc., Ph.D.

---

Repository structure

Analyses are organised by data domain. Statistics are in R; confocal image processing and the post-mortem brain-ELISA / Western-blot quantification are in Python.

NETs-PD/
├── PPMI-Baseline/            # PPMI baseline cohort (Figure 1, Suppl. S1–S3)
│   ├── Fig1bc_S1_S3a.r                       # baseline differential expression
│   ├── PPMI-BL-visit_Volcano.ipynb           # baseline volcano
│   ├── Baseline_Neutrophil_Deconvolution.R   # CIBERSORTx + per-cell PADI4 (Suppl. S2)
│   ├── Baseline_ROC_Biomarkers.R             # diagnostic ROC panel (Suppl. S1)
│   └── Baseline_Clinical_Correlations.R      # DaT / UPDRS correlations (Suppl. S3)
├── PPMI-Longitudinal/        # PPMI longitudinal cohort (Figure 2, Suppl. S5)
│   ├── Fig2_S3.r                             # longitudinal mixed-model trajectories
│   ├── PPMI-all-visit_NETs.ipynb             # all-visit preparation
│   ├── Longitudinal_Trajectories.R           # random-slope LMM + Bayesian (Figure 2)
│   └── Longitudinal_Stability.R              # ICC / variance partition / Bayesian ICC (Suppl. S5)
├── Regional-Cohort/          # serum + post-mortem brain cohorts (Figures 3–4, Suppl. WB)
│   ├── Serum_MPO-DNA_ELISA.R                 # serum MPO-DNA (Figure 3a)
│   ├── Serum_CitH3-DNA_ELISA.R               # serum CitH3-DNA (Figure 3b)
│   ├── Serum_Biomarker_ROC.R                 # serum biomarker ROC (Figure 3)
│   ├── Brain_ELISA_3Markers.py               # brain MPO/NE/CitH3-DNA + composite (Figure 4)
│   └── Western_Blot_MPO_60kDa.py             # mature MPO ~60 kDa re-quantification (Suppl. WB)
└── Confocal-Image-Pipeline/  # post-mortem confocal NETs in cortex + substantia nigra (Figure 5)
    └── (see Confocal-Image-Pipeline/README.md for the ordered pipeline)

Data

PPMI transcriptomic and clinical data are controlled-access and available from the PPMI upon application; human serum and post-mortem brain data are available from the corresponding author on reasonable request.

Software

• R ≥ 4.4 — tidyverse, lme4/lmerTest, emmeans, sandwich, boot, brms, performance, pROC, glmnet, limma/edgeR, WRS2.
• Python ≥ 3.11 — numpy, pandas, scipy, statsmodels, scikit-image, cellpose, napari, aicsimageio/Bio-Formats, tifffile.

Statistical framework (applied throughout): covariate-adjusted (mixed-effects) models with HC3 robust standard errors, estimated marginal means with Holm adjustment, stratified/cluster bootstrap confidence intervals, and Bayesian sensitivity analyses with weakly informative priors (primary inference for the small post-mortem cohorts).

Citation

Movement Disorders · Published article ·

If you use this repository, please cite the following article:

Nguyen HD, Lee S, Ma H-I, Kim YJ, Kim H-J, Kim YE. Longitudinal systemic transcriptomic profiling and neuropathological deposition of neutrophil extracellular traps in Parkinson’s disease. Movement Disorders. 2026. https://doi.org/10.1002/mds.70443

Citation metrics

Acknowledgment

We thank the participants and the clinical and research teams at Hallym University Sacred Heart Hospital, Hallym University, Seoul National University Hospital, and Yongin Severance Hospital for their contributions to this study.

We gratefully acknowledge the participants, investigators, and study teams of the Parkinson’s Progression Markers Initiative (PPMI) for making the longitudinal clinical and transcriptomic data used in this work available to the research community. PPMI is funded by The Michael J. Fox Foundation for Parkinson’s Research and its funding partners.

License

This repository is released under the GNU General Public License v3.0.